Value of Cargo Pooling in On-Demand Intra-City Freight Logistics

Abstract

On-demand intra-city freight logistics (ICFL) has recently emerged as a new freight service, where shippers can submit their shipping requests using smartphones and be matched to drivers in real time based on their locations and drivers’ availability. A major challenge faced by on-demand ICFL platforms is the shortage of vehicles during peak demand periods. Cargo pooling, the cargo version of carpooling, offers as a promising way to increase supply: cargoes heading in the same direction would share the cargo compartment of the same vehicle and be serviced simultaneously, which is achieved by careful sequencing of the pickup and delivery locations of the cargoes. We investigate models for cargo pooling for on-demand ICFL and quantify its benefit, which is new to the literature. The major difference between existing studies on ICFL and ours is that we no longer assume that demands are known beforehand. Instead, the demands arrive gradually throughout the day and we need to periodically match requests to drivers and re-optimize vehicle routes. We formulate the matching problem as a dynamic pickup and delivery problem with three-dimensional loading and time window constraints. To solve this model, we develop an algorithm based on large neighborhood search and tree search. The algorithm is tested with real freight data in a city in the Yangtze River Delta. Results show that the algorithm can reduce the total cost by 21.4% and reduce the total vehicle miles traveled by 36.0%.